Although real-world images usually have defocus blur, most computer vision applications are supposed to cooperate with all-in-focus (AiF) images.
Therefore, more and more attention is given on shallow-DoF image deblurring and multi-focus image fusion.
A multi-focus image fusion approach [1]} applies Laplacian to different scales of images.
Zhan et al.  [2]} proposed a guided filter to help edge preserving during multi-focus image fusion.
Nejati et al.  [3]} learn a sparse representation of relative sharpness measurement and produce a pixel-level score map for decision through pooling.
An unsupervised learning approach  [4]} was proposed to fuse either multi-exposure or multi-focus images to generate a high dynamic range (HDR) or an AiF image. Liu et al.  [5]} proposed to learn a focus map and a segmentation map through deep neural networks, and fuse images by integrating these maps.
